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Article

Abstract

Water managers in western U.S., including areas such as the State of Utah, are challenged with managing scarce resources and thus, rely heavily on forecasts to allocate and meet various water demands. The need for improved streamflow and snowpack forecast models in the Upper Colorado River and Great Basin is of the utmost importance. In this research, the use of oceanic and climatic variables as predictors to improve the long lead-time (three to nine months) forecast of streamflow and snowpack was investigated. Singular Value Decomposition (SVD) analysis was used to identify a region of Pacific Ocean SSTs and a region of 500 mbar geopotential height (Z500) that were teleconnected with streamflow (and snowpack) in Upper Colorado River and Great Basin headwaters. The resulting Pacific Ocean SSTs and Z500 regions were used to create indices that were then used as predictors in a non-parametric forecasting model. The majority of forecasts resulted in positive statistical skill, which indicated an improvement of the forecast over the climatology or no-skill forecast. The results indicated that derived indices from Pacific Ocean SSTs were better suited for long lead-time (six to nine month) forecasts of streamflow (and snowpack) while the derived indices from Z500 improved short-lead time (3 month) forecasts. In all, the results of the forecast model indicated that incorporating Pacific oceanic-atmospheric climatic variability in forecast models can lead to improved forecasts for both streamflow and snowpack.

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